Title
Spatially varying parameter estimation for dust emissions using reduced-tangent-linearization 4DVar
Author
Jin, J.
Lin, H.X.
Heemink, A.
Segers, A.
Publication year
2018
Abstract
In previous studies, a number of model-based dust forecasts and early warning systems have been developed for the prevention of environmental impacts due to dusts. However, the accuracy of the model is limited by imperfect identification of dust emissions, in particular by the friction velocity threshold parameterization in the emission process. In this study, an integrated dust storm forecast system - LOTOS-EUROS/Dust coupled with reduced-tangent-linearization 4DVar data assimilation has been developed. In order to overcome the inflexibility and inaccuracy of the existing friction velocity threshold parameterization in large-scale models, a spatially varying multiplicative factor for the threshold is introduced. This parameter is estimated by assimilating measurements from a field station network developed by China Ministry of Environmental Protection. The data assimilation algorithm is adjoint-free, and its computational complexity increases with the number of uncertain parameters. Two model reducing techniques, sensitivity-based parameter filters and proper orthogonal decomposition, are sequentially implemented after each other, which lead to a reduction of parameter dimension from initially O(104) to O(102). Twin experiments are conducted to evaluate the impact of assimilation settings on the dust forecast accuracy. In addition, experiments with real observations are conducted. However, these observations also reflect the aerosol concentration from local emissions. To effectively use these observations as representative for dust concentrations, an observation bias correction and a variable representation error are designed. Improvements on the dust storm forecast with our system are demonstrated. © 2018 Elsevier Ltd
Subject
Environment & Sustainability
Environment
Urbanisation
Adjoint-free
Dust emission
Friction velocity threshold
Observation bias correction
Parameter filters
Reduced-tangent-linearization
To reference this document use:
http://resolver.tudelft.nl/uuid:875c9cde-4b07-41e7-96fe-91d70438396b
DOI
https://doi.org/10.1016/j.atmosenv.2018.05.060
TNO identifier
810204
ISSN
1352-2310
Source
Atmospheric Environment, 187, 358-373
Document type
article